Presentation for the San Francisco #IDCC14 conference (http://www.dcc.ac.uk/events/idcc14/day-two-papers). The presentation covers publishing zooarchaeology data with Open Context (http://opencontext.org) to study the spread of farming from the Near East to Europe through Anatolia. It looks at editorial processes, linked data annotation, and other workflow concerns relating to making raw data more usable for comparative analysis.

1. Publishing and Pushing:
Mixing Models for Communicating
Research Data in Archaeology
Eric C. Kansa (@ekansa)
UC Berkeley D-Lab
& Open Context
Sarah Whitcher Kansa
Benjamin Arbuckle
The Alexandria Archive Institute
& Open Context
University of North Carolina,
Chapel Hill
Unless otherwise indicated, this work is licensed under a Creative Commons
Attribution 3.0 License <http://creativecommons.org/licenses/by/3.0/>

2. Introduction
Challenges in Reusing Data
1. Background
2. Data publishing workflow
3. Data curation and dynamism

5. Need more carrots!
1. Citation, credit, intellectu
ally valued
2. Research outcomes
(new insights from data
reuse!)

6. EOL Computable Data
Challenge
(Ben Arbuckle, Sarah
W. Kansa, Eric Kansa)

8. Large scale data sharing &
integration for exploring the
origins of farming.
Funded by EOL / NEH

9. 1. 300,000 bone specimens
2. Complex: dozens, up to 110
descriptive fields
3. 34 contributors from 15
archaeological sites
4. More than 4 person years
of effort to create the data !

10. Relatively collaborative
bunch, Ben Arbuckle cultivated
relationships & built trust over
years prior to EOL funding.

11. “204: Dynamics of Data Reuse when Aggregating Data through Time and
Space: The Case of Archaeology and Zoology”
Elizabeth Yakel; Ixchel Faniel; Rebecca Frank

12. Introduction
Challenges in Reusing Data
1. Background
2. Data publishing workflow
3. Data curation and dynamism

13. 1. Referenced by US National
Science Foundation and
National Endowment for the
Humanities for Data
Management
2. “Data sharing as
publishing” metaphor

14. Raw Data:
Idiosyncratic, sometimes
highly coded, often
inconsistent

15. Raw Data Can Be Unappetizing

16. Publishing Workflow
Improve / Enhance
1.
2.
Consistency
Context
(intelligibility)

17. Sometimes data is better
served cooked

18. - Documentation
- Review, editing
- Annotation

19. - Documentation
- Review, editing
- Annotation

20. - Documentation
- Review, editing
- Annotation

21. - Documentation
- Review, editing
- Annotation

22. - Documentation
- Review, editing
- Annotation

23. “Ovis orientalis”
Sheep,
wild
Code: 16
Wild
sheep
O.
orientalis
Code: 70
Ovis orientalis
Code: 15
Code: 14
Sheep
(wild)

24. - Documentation
- Review, editing
- Annotation

25. “Ovis orientalis”
http://eol.org/pages/311906/
Sheep,
wild
Code: 16
Wild
sheep
O.
orientalis
Code: 70
Ovis orientalis
Code: 15
Code: 14
Sheep
(wild)

26. ●
●
Controlled vocabulary
Linked Data applications

27. “Sheep/goat”
http://eol.org/pages/32609438/
1. Needed to mint new
concepts like
“sheep/goat”
2. Vocabularies need to
be responsive for
multidisciplinary
applications

30. Linking to UBERON
1. Needed a controlled vocabulary for
bone anatomy
2. Better data modeling than common in
zooarchaeology, adds quality.

31. Linking to UBERON
1. Models links between anatomy,
developmental biology, and genetics
2. Unexpected links between the
Humanities and Bioinformatics!

34. 6500 BC (few pigs, mixing with wild animals?)
7500 BC (sheep + goat dominate, few pigs, few cattle)
7000 BC (many pigs, cattle)
8000 BC (cattle, pigs,
sheep + goats)
• Not a neat model of progress to adopt a more productive
economy. Very different, sometimes piecemeal adoption in
different regions.
• Separate coastal and inland routes for the spread of domestic
animals, over a 1000-year time period.

35. Easy to Align
1. Animal taxonomy
2. Bone anatomy
3. Sex determinations
4. Side of the animal
5. Fusion (bone growth, up to
a point)

36. Hard to Align (poor modeling, recording)
1. Tooth wear (age)
2. Fusion data
3. Measurements
Despite common research methods!!

37. “Under the hood” exposure
will lead to better data
documentation practices?

38. Nobody expected their data
to see wider scrutiny either..

39. Professional expectations for data reuse
1. Need better data modeling
(than feasible
with, cough, Excel)
2. Data
validation, normalization
3. Requires training &
incentives for researchers
to care more about quality
of their data!

40. Data are challenging!
1.
2.
3.
4.
5.
Decoding takes 10x longer
Data management plans should
also cover data modeling, quality
control (esp. validation)
More work needed modeling
research methods (esp. sampling)
Editing, annotation requires lots of
back-and-forth with data authors
Data needs investment to be
useful!

41. Introduction
Challenges in Reusing Data
1. Background
2. Data publishing workflow
3. Data curation and dynamism

42. Investing in Data is a Continual Need
1.
2.
3.
4.
Data and code co-evolve. New
visualizations, analysis may reveal
unseen problems in data.
Data and metadata change routinely
(revised stratigraphy requires ongoing
updates to data in this analysis)
Problems, interpretive issues in data
(and annotations) keep cropping up.
Is publishing a bad metaphor implying
a static product?

44. Data sharing as publication
Data sharing as open source
release cycles?

45. Data sharing as publication
Data sharing as open source
release cycles?

46. Data sharing as publication
AND
Data sharing as open source
release cycles

47. One does not simply
walk into Mordor
Academia and share
usable data…
Image Credit: Copyright Newline Cinema

48. Final Thoughts
Data require intellectual
investment, methodological and
theoretical innovation.
Institutional structures poorly
configured to support data
powered research
New professional roles
needed, but who will pay for it?

49. Thank you!
IDCC reviewers
(excellent, very helpful
comments!)